Stephen Casper

Publication Activity (10 Years)

Years Active: 2019-2024
Publications (10 Years): 31

Top Topics

Diffusion Models

Reinforcement Learning

Top Venues

Publications

Stephen Casper, Carson Ezell, Charlotte Siegmann, Noam Kolt, Taylor Lynn Curtis, Benjamin Bucknall, Andreas A. Haupt, Kevin Wei, Jérémy Scheurer, Marius Hobbhahn, Lee Sharkey, Satyapriya Krishna, Marvin Von Hagen, Silas Alberti, Alan Chan, Qinyi Sun, Michael Gerovitch, David Bau, Max Tegmark, David Krueger, Dylan Hadfield-Menell
Black-Box Access is Insufficient for Rigorous AI Audits. FAccT (2024)
Stephen Casper, Carson Ezell, Charlotte Siegmann, Noam Kolt, Taylor Lynn Curtis, Benjamin Bucknall, Andreas Alexander Haupt, Kevin Wei, Jérémy Scheurer, Marius Hobbhahn, Lee Sharkey, Satyapriya Krishna, Marvin Von Hagen, Silas Alberti, Alan Chan, Qinyi Sun, Michael Gerovitch, David Bau, Max Tegmark, David Krueger, Dylan Hadfield-Menell
Black-Box Access is Insufficient for Rigorous AI Audits. CoRR (2024)
Stephen Casper, Lennart Schulze, Oam Patel, Dylan Hadfield-Menell
Defending Against Unforeseen Failure Modes with Latent Adversarial Training. CoRR (2024)
Abhay Sheshadri, Aidan Ewart, Phillip Guo, Aengus Lynch, Cindy Wu, Vivek Hebbar, Henry Sleight, Asa Cooper Stickland, Ethan Perez, Dylan Hadfield-Menell, Stephen Casper
Targeted Latent Adversarial Training Improves Robustness to Persistent Harmful Behaviors in LLMs. CoRR (2024)
Aengus Lynch, Phillip Guo, Aidan Ewart, Stephen Casper, Dylan Hadfield-Menell
Eight Methods to Evaluate Robust Unlearning in LLMs. CoRR (2024)
Stephen Casper, Jieun Yun, Joonhyuk Baek, Yeseong Jung, Minhwan Kim, Kiwan Kwon, Saerom Park, Hayden Moore, David Shriver, Marissa Connor, Keltin Grimes, Angus Nicolson, Arush Tagade, Jessica Rumbelow, Hieu Minh Nguyen, Dylan Hadfield-Menell
The SaTML '24 CNN Interpretability Competition: New Innovations for Concept-Level Interpretability. CoRR (2024)
Usman Anwar, Abulhair Saparov, Javier Rando, Daniel Paleka, Miles Turpin, Peter Hase, Ekdeep Singh Lubana, Erik Jenner, Stephen Casper, Oliver Sourbut, Benjamin L. Edelman, Zhaowei Zhang, Mario Günther, Anton Korinek, José Hernández-Orallo, Lewis Hammond, Eric J. Bigelow, Alexander Pan, Lauro Langosco, Tomasz Korbak, Heidi Zhang, Ruiqi Zhong, Seán Ó hÉigeartaigh, Gabriel Recchia, Giulio Corsi, Alan Chan, Markus Anderljung, Lilian Edwards, Yoshua Bengio, Danqi Chen, Samuel Albanie, Tegan Maharaj, Jakob N. Foerster, Florian Tramèr, He He, Atoosa Kasirzadeh, Yejin Choi, David Krueger
Foundational Challenges in Assuring Alignment and Safety of Large Language Models. CoRR (2024)
Anka Reuel, Ben Bucknall, Stephen Casper, Tim Fist, Lisa Soder, Onni Aarne, Lewis Hammond, Lujain Ibrahim, Alan Chan, Peter Wills, Markus Anderljung, Ben Garfinkel, Lennart Heim, Andrew Trask, Gabriel Mukobi, Rylan Schaeffer, Mauricio Baker, Sara Hooker, Irene Solaiman, Alexandra Sasha Luccioni, Nitarshan Rajkumar, Nicolas Moës, Jeffrey Ladish, Neel Guha, Jessica Newman, Yoshua Bengio, Tobin South, Alex Pentland, Sanmi Koyejo, Mykel J. Kochenderfer, Robert Trager
Open Problems in Technical AI Governance. CoRR (2024)
Sijia Liu, Yuanshun Yao, Jinghan Jia, Stephen Casper, Nathalie Baracaldo, Peter Hase, Xiaojun Xu, Yuguang Yao, Hang Li, Kush R. Varshney, Mohit Bansal, Sanmi Koyejo, Yang Liu
Rethinking Machine Unlearning for Large Language Models. CoRR (2024)
Rusheb Shah, Quentin Feuillade-Montixi, Soroush Pour, Arush Tagade, Stephen Casper, Javier Rando
Scalable and Transferable Black-Box Jailbreaks for Language Models via Persona Modulation. CoRR (2023)
Stephen Casper, Yuxiao Li, Jiawei Li, Tong Bu, Kevin Zhang, Dylan Hadfield-Menell
Benchmarking Interpretability Tools for Deep Neural Networks. CoRR (2023)
Stephen Casper, Jason Lin, Joe Kwon, Gatlen Culp, Dylan Hadfield-Menell
Explore, Establish, Exploit: Red Teaming Language Models from Scratch. CoRR (2023)
Stephen Casper, Zifan Guo, Shreya Mogulothu, Zachary Marinov, Chinmay Deshpande, Rui-Jie Yew, Zheng Dai, Dylan Hadfield-Menell
Measuring the Success of Diffusion Models at Imitating Human Artists. CoRR (2023)
Stephen Casper, Xander Davies, Claudia Shi, Thomas Krendl Gilbert, Jérémy Scheurer, Javier Rando, Rachel Freedman, Tomasz Korbak, David Lindner, Pedro Freire, Tony Tong Wang, Samuel Marks, Charbel-Raphaël Ségerie, Micah Carroll, Andi Peng, Phillip J. K. Christoffersen, Mehul Damani, Stewart Slocum, Usman Anwar, Anand Siththaranjan, Max Nadeau, Eric J. Michaud, Jacob Pfau, Dmitrii Krasheninnikov, Xin Chen, Lauro Langosco, Peter Hase, Erdem Biyik, Anca D. Dragan, David Krueger, Dorsa Sadigh, Dylan Hadfield-Menell
Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback. Trans. Mach. Learn. Res. 2023 (2023)
Kevin Liu, Stephen Casper, Dylan Hadfield-Menell, Jacob Andreas
Cognitive Dissonance: Why Do Language Model Outputs Disagree with Internal Representations of Truthfulness? CoRR (2023)
Stephen Casper, Xander Davies, Claudia Shi, Thomas Krendl Gilbert, Jérémy Scheurer, Javier Rando, Rachel Freedman, Tomasz Korbak, David Lindner, Pedro Freire, Tony Wang, Samuel Marks, Charbel-Raphaël Ségerie, Micah Carroll, Andi Peng, Phillip J. K. Christoffersen, Mehul Damani, Stewart Slocum, Usman Anwar, Anand Siththaranjan, Max Nadeau, Eric J. Michaud, Jacob Pfau, Dmitrii Krasheninnikov, Xin Chen, Lauro Langosco, Peter Hase, Erdem Biyik, Anca D. Dragan, David Krueger, Dorsa Sadigh, Dylan Hadfield-Menell
Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback. CoRR (2023)
Stephen Casper, Tong Bu, Yuxiao Li, Jiawei Li, Kevin Zhang, Kaivalya Hariharan, Dylan Hadfield-Menell
Red Teaming Deep Neural Networks with Feature Synthesis Tools. NeurIPS (2023)
Tilman Räuker, Anson Ho, Stephen Casper, Dylan Hadfield-Menell
Toward Transparent AI: A Survey on Interpreting the Inner Structures of Deep Neural Networks. SaTML (2023)
Stephen Casper, Dylan Hadfield-Menell, Gabriel Kreiman
White-Box Adversarial Policies in Deep Reinforcement Learning. SafeAI@AAAI (2023)
Kevin Liu, Stephen Casper, Dylan Hadfield-Menell, Jacob Andreas
Cognitive Dissonance: Why Do Language Model Outputs Disagree with Internal Representations of Truthfulness? EMNLP (2023)
Stephen Casper, Kaivalya Hariharan, Dylan Hadfield-Menell
Diagnostics for Deep Neural Networks with Automated Copy/Paste Attacks. CoRR (2022)
Tilman Räuker, Anson Ho, Stephen Casper, Dylan Hadfield-Menell
Toward Transparent AI: A Survey on Interpreting the Inner Structures of Deep Neural Networks. CoRR (2022)
Stephen Casper, Dylan Hadfield-Menell, Gabriel Kreiman
White-Box Adversarial Policies in Deep Reinforcement Learning. CoRR (2022)
Stephen Casper, Max Nadeau, Dylan Hadfield-Menell, Gabriel Kreiman
Robust Feature-Level Adversaries are Interpretability Tools. NeurIPS (2022)
Stephen Casper, Max Nadeau, Gabriel Kreiman
One Thing to Fool them All: Generating Interpretable, Universal, and Physically-Realizable Adversarial Features. CoRR (2021)
Stephen Casper, Xavier Boix, Vanessa D'Amario, Ling Guo, Martin Schrimpf, Kasper Vinken, Gabriel Kreiman
Frivolous Units: Wider Networks Are Not Really That Wide. AAAI (2021)
Shlomi Hod, Stephen Casper, Daniel Filan, Cody Wild, Andrew Critch, Stuart Russell
Detecting Modularity in Deep Neural Networks. CoRR (2021)
Daniel Filan, Stephen Casper, Shlomi Hod, Cody Wild, Andrew Critch, Stuart Russell
Clusterability in Neural Networks. CoRR (2021)
Stephen Casper
The Achilles Heel Hypothesis: Pitfalls for AI Systems via Decision Theoretic Adversaries. CoRR (2020)
Abdelrhman Saleh, Tovly Deutsch, Stephen Casper, Yonatan Belinkov, Stuart M. Shieber
Probing Neural Dialog Models for Conversational Understanding. CoRR (2020)
Stephen Casper, Xavier Boix, Vanessa D'Amario, Ling Guo, Martin Schrimpf, Kasper Vinken, Gabriel Kreiman
Removable and/or Repeated Units Emerge in Overparametrized Deep Neural Networks. CoRR (2019)